Overview

Dataset statistics

Number of variables36
Number of observations1677
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory452.1 KiB
Average record size in memory276.1 B

Variable types

Numeric17
Categorical18
Boolean1

Alerts

Age is highly overall correlated with TotalWorkingYears and 1 other fieldsHigh correlation
DistanceFromHome is highly overall correlated with DistanceFromHome_outHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYears and 1 other fieldsHigh correlation
NumCompaniesWorked is highly overall correlated with YearsInJobs and 1 other fieldsHigh correlation
TotalWorkingYears is highly overall correlated with Age and 6 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 5 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with TotalWorkingYears and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompanyHigh correlation
YearsWithCurrManager is highly overall correlated with TotalWorkingYears and 4 other fieldsHigh correlation
CurrManagerTotal is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsInJobs is highly overall correlated with NumCompaniesWorked and 4 other fieldsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
DistanceFromHome_out is highly overall correlated with DistanceFromHomeHigh correlation
Age_out is highly overall correlated with AgeHigh correlation
JobHopper is highly overall correlated with NumCompaniesWorkedHigh correlation
DistanceFromHome_out is highly imbalanced (79.8%)Imbalance
Age_out is highly imbalanced (89.9%)Imbalance
TrainingTimesLastYear has 50 (3.0%) zerosZeros
YearsAtCompany has 54 (3.2%) zerosZeros
YearsInCurrentRole has 307 (18.3%) zerosZeros
YearsSinceLastPromotion has 726 (43.3%) zerosZeros
YearsWithCurrManager has 298 (17.8%) zerosZeros
CurrManagerTotal has 298 (17.8%) zerosZeros

Reproduction

Analysis started2023-03-06 20:42:38.935980
Analysis finished2023-03-06 20:44:26.458910
Duration1 minute and 47.52 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.036971
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:26.643374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median35
Q341
95-th percentile52
Maximum60
Range42
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5071116
Coefficient of variation (CV)0.23606622
Kurtosis-0.13284552
Mean36.036971
Median Absolute Deviation (MAD)6
Skewness0.45409276
Sum60434
Variance72.370947
MonotonicityNot monotonic
2023-03-06T21:44:26.929242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36 101
 
6.0%
29 96
 
5.7%
34 95
 
5.7%
31 90
 
5.4%
35 89
 
5.3%
38 88
 
5.2%
40 79
 
4.7%
27 67
 
4.0%
30 66
 
3.9%
28 62
 
3.7%
Other values (33) 844
50.3%
ValueCountFrequency (%)
18 12
 
0.7%
19 13
 
0.8%
20 6
 
0.4%
21 16
 
1.0%
22 14
 
0.8%
23 20
 
1.2%
24 23
 
1.4%
25 36
2.1%
26 45
2.7%
27 67
4.0%
ValueCountFrequency (%)
60 3
 
0.2%
59 10
0.6%
58 9
0.5%
57 4
 
0.2%
56 10
0.6%
55 17
1.0%
54 7
 
0.4%
53 15
0.9%
52 18
1.1%
51 14
0.8%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Travel_Rarely
1290 
Travel_Frequently
261 
Non-Travel
 
126

Length

Max length17
Median length13
Mean length13.397138
Min length10

Characters and Unicode

Total characters22467
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Frequently
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 1290
76.9%
Travel_Frequently 261
 
15.6%
Non-Travel 126
 
7.5%

Length

2023-03-06T21:44:27.246890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:27.543146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1290
76.9%
travel_frequently 261
 
15.6%
non-travel 126
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 3489
15.5%
r 3228
14.4%
l 3228
14.4%
a 2967
13.2%
T 1677
7.5%
v 1677
7.5%
y 1551
6.9%
_ 1551
6.9%
R 1290
 
5.7%
n 387
 
1.7%
Other values (7) 1422
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17436
77.6%
Uppercase Letter 3354
 
14.9%
Connector Punctuation 1551
 
6.9%
Dash Punctuation 126
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3489
20.0%
r 3228
18.5%
l 3228
18.5%
a 2967
17.0%
v 1677
9.6%
y 1551
8.9%
n 387
 
2.2%
q 261
 
1.5%
u 261
 
1.5%
t 261
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
T 1677
50.0%
R 1290
38.5%
F 261
 
7.8%
N 126
 
3.8%
Connector Punctuation
ValueCountFrequency (%)
_ 1551
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20790
92.5%
Common 1677
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3489
16.8%
r 3228
15.5%
l 3228
15.5%
a 2967
14.3%
T 1677
8.1%
v 1677
8.1%
y 1551
7.5%
R 1290
 
6.2%
n 387
 
1.9%
F 261
 
1.3%
Other values (5) 1035
 
5.0%
Common
ValueCountFrequency (%)
_ 1551
92.5%
- 126
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3489
15.5%
r 3228
14.4%
l 3228
14.4%
a 2967
13.2%
T 1677
7.5%
v 1677
7.5%
y 1551
6.9%
_ 1551
6.9%
R 1290
 
5.7%
n 387
 
1.7%
Other values (7) 1422
6.3%

DailyRate
Real number (ℝ)

Distinct625
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean892.74955
Minimum107
Maximum3921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:27.875181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile277
Q1589
median890
Q31223
95-th percentile1443
Maximum3921
Range3814
Interquartile range (IQR)634

Descriptive statistics

Standard deviation374.49626
Coefficient of variation (CV)0.41948636
Kurtosis1.3832517
Mean892.74955
Median Absolute Deviation (MAD)316
Skewness0.16161078
Sum1497141
Variance140247.45
MonotonicityNot monotonic
2023-03-06T21:44:28.243785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1082 16
 
1.0%
775 12
 
0.7%
465 11
 
0.7%
855 11
 
0.7%
1329 10
 
0.6%
1157 10
 
0.6%
806 10
 
0.6%
827 10
 
0.6%
989 9
 
0.5%
658 9
 
0.5%
Other values (615) 1569
93.6%
ValueCountFrequency (%)
107 1
 
0.1%
111 1
 
0.1%
115 2
 
0.1%
116 1
 
0.1%
117 5
0.3%
118 1
 
0.1%
119 3
0.2%
124 2
 
0.1%
130 4
0.2%
135 1
 
0.1%
ValueCountFrequency (%)
3921 1
 
0.1%
1499 2
 
0.1%
1498 1
 
0.1%
1495 4
0.2%
1492 1
 
0.1%
1490 4
0.2%
1489 1
 
0.1%
1488 1
 
0.1%
1485 5
0.3%
1482 1
 
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Research & Development
1167 
Sales
471 
Human Resources
 
39

Length

Max length22
Median length22
Mean length17.062612
Min length5

Characters and Unicode

Total characters28614
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowSales
3rd rowSales
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 1167
69.6%
Sales 471
28.1%
Human Resources 39
 
2.3%

Length

2023-03-06T21:44:28.612128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:28.930656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
research 1167
28.8%
1167
28.8%
development 1167
28.8%
sales 471
11.6%
human 39
 
1.0%
resources 39
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 6384
22.3%
2373
 
8.3%
s 1716
 
6.0%
a 1677
 
5.9%
l 1638
 
5.7%
R 1206
 
4.2%
r 1206
 
4.2%
c 1206
 
4.2%
n 1206
 
4.2%
m 1206
 
4.2%
Other values (10) 8796
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22191
77.6%
Uppercase Letter 2883
 
10.1%
Space Separator 2373
 
8.3%
Other Punctuation 1167
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6384
28.8%
s 1716
 
7.7%
a 1677
 
7.6%
l 1638
 
7.4%
r 1206
 
5.4%
c 1206
 
5.4%
n 1206
 
5.4%
m 1206
 
5.4%
o 1206
 
5.4%
p 1167
 
5.3%
Other values (4) 3579
16.1%
Uppercase Letter
ValueCountFrequency (%)
R 1206
41.8%
D 1167
40.5%
S 471
 
16.3%
H 39
 
1.4%
Space Separator
ValueCountFrequency (%)
2373
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25074
87.6%
Common 3540
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6384
25.5%
s 1716
 
6.8%
a 1677
 
6.7%
l 1638
 
6.5%
R 1206
 
4.8%
r 1206
 
4.8%
c 1206
 
4.8%
n 1206
 
4.8%
m 1206
 
4.8%
o 1206
 
4.8%
Other values (8) 6423
25.6%
Common
ValueCountFrequency (%)
2373
67.0%
& 1167
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6384
22.3%
2373
 
8.3%
s 1716
 
6.0%
a 1677
 
5.9%
l 1638
 
5.7%
R 1206
 
4.2%
r 1206
 
4.2%
c 1206
 
4.2%
n 1206
 
4.2%
m 1206
 
4.2%
Other values (10) 8796
30.7%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6839595
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:29.215466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q312
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.8261426
Coefficient of variation (CV)0.90121823
Kurtosis0.10082791
Mean8.6839595
Median Absolute Deviation (MAD)5
Skewness1.0715877
Sum14563
Variance61.248507
MonotonicityNot monotonic
2023-03-06T21:44:29.499546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 278
16.6%
1 237
14.1%
8 104
 
6.2%
9 101
 
6.0%
10 99
 
5.9%
3 95
 
5.7%
7 91
 
5.4%
6 78
 
4.7%
5 74
 
4.4%
4 64
 
3.8%
Other values (19) 456
27.2%
ValueCountFrequency (%)
1 237
14.1%
2 278
16.6%
3 95
 
5.7%
4 64
 
3.8%
5 74
 
4.4%
6 78
 
4.7%
7 91
 
5.4%
8 104
 
6.2%
9 101
 
6.0%
10 99
 
5.9%
ValueCountFrequency (%)
29 27
1.6%
28 26
1.6%
27 9
 
0.5%
26 24
1.4%
25 26
1.6%
24 28
1.7%
23 24
1.4%
22 16
1.0%
21 18
1.1%
20 25
1.5%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
683 
4
464 
2
304 
1
182 
5
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

Length

2023-03-06T21:44:29.798174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:30.098392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

Most occurring characters

ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 683
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%

EducationField
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Life Sciences
775 
Medical
549 
Marketing
152 
Technical Degree
106 
Other
82 

Length

Max length16
Median length15
Mean length10.487179
Min length5

Characters and Unicode

Total characters17587
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical
2nd rowOther
3rd rowMarketing
4th rowMedical
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 775
46.2%
Medical 549
32.7%
Marketing 152
 
9.1%
Technical Degree 106
 
6.3%
Other 82
 
4.9%
Human Resources 13
 
0.8%

Length

2023-03-06T21:44:30.412444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:30.732058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
life 775
30.1%
sciences 775
30.1%
medical 549
21.4%
marketing 152
 
5.9%
technical 106
 
4.1%
degree 106
 
4.1%
other 82
 
3.2%
human 13
 
0.5%
resources 13
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 3558
20.2%
i 2357
13.4%
c 2324
13.2%
n 1046
 
5.9%
894
 
5.1%
a 820
 
4.7%
s 801
 
4.6%
L 775
 
4.4%
f 775
 
4.4%
S 775
 
4.4%
Other values (16) 3462
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14122
80.3%
Uppercase Letter 2571
 
14.6%
Space Separator 894
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3558
25.2%
i 2357
16.7%
c 2324
16.5%
n 1046
 
7.4%
a 820
 
5.8%
s 801
 
5.7%
f 775
 
5.5%
l 655
 
4.6%
d 549
 
3.9%
r 353
 
2.5%
Other values (7) 884
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
L 775
30.1%
S 775
30.1%
M 701
27.3%
T 106
 
4.1%
D 106
 
4.1%
O 82
 
3.2%
H 13
 
0.5%
R 13
 
0.5%
Space Separator
ValueCountFrequency (%)
894
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16693
94.9%
Common 894
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3558
21.3%
i 2357
14.1%
c 2324
13.9%
n 1046
 
6.3%
a 820
 
4.9%
s 801
 
4.8%
L 775
 
4.6%
f 775
 
4.6%
S 775
 
4.6%
M 701
 
4.2%
Other values (15) 2761
16.5%
Common
ValueCountFrequency (%)
894
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3558
20.2%
i 2357
13.4%
c 2324
13.2%
n 1046
 
5.9%
894
 
5.1%
a 820
 
4.7%
s 801
 
4.6%
L 775
 
4.4%
f 775
 
4.4%
S 775
 
4.4%
Other values (16) 3462
19.7%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
4
537 
3
496 
2
345 
1
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Length

2023-03-06T21:44:31.034035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:31.332557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring characters

ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Male
1064 
Female
613 

Length

Max length6
Median length4
Mean length4.7310674
Min length4

Characters and Unicode

Total characters7934
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 1064
63.4%
Female 613
36.6%

Length

2023-03-06T21:44:31.631221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:31.930840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male 1064
63.4%
female 613
36.6%

Most occurring characters

ValueCountFrequency (%)
e 2290
28.9%
a 1677
21.1%
l 1677
21.1%
M 1064
13.4%
F 613
 
7.7%
m 613
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6257
78.9%
Uppercase Letter 1677
 
21.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2290
36.6%
a 1677
26.8%
l 1677
26.8%
m 613
 
9.8%
Uppercase Letter
ValueCountFrequency (%)
M 1064
63.4%
F 613
36.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7934
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2290
28.9%
a 1677
21.1%
l 1677
21.1%
M 1064
13.4%
F 613
 
7.7%
m 613
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2290
28.9%
a 1677
21.1%
l 1677
21.1%
M 1064
13.4%
F 613
 
7.7%
m 613
 
7.7%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.79845
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:32.195176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile36
Q151
median69
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.435928
Coefficient of variation (CV)0.28667216
Kurtosis-1.1356881
Mean67.79845
Median Absolute Deviation (MAD)16
Skewness-0.12030233
Sum113698
Variance377.7553
MonotonicityNot monotonic
2023-03-06T21:44:32.552738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 41
 
2.4%
84 41
 
2.4%
96 39
 
2.3%
66 39
 
2.3%
78 39
 
2.3%
72 37
 
2.2%
82 37
 
2.2%
41 35
 
2.1%
95 35
 
2.1%
62 35
 
2.1%
Other values (61) 1299
77.5%
ValueCountFrequency (%)
30 13
0.8%
31 5
 
0.3%
32 23
1.4%
33 13
0.8%
34 6
 
0.4%
35 16
1.0%
36 14
0.8%
37 16
1.0%
38 5
 
0.3%
39 12
0.7%
ValueCountFrequency (%)
100 23
1.4%
99 14
 
0.8%
98 32
1.9%
97 26
1.6%
96 39
2.3%
95 35
2.1%
94 21
1.3%
93 18
1.1%
92 29
1.7%
91 24
1.4%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
1107 
2
358 
4
139 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Length

2023-03-06T21:44:32.899008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:33.196836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring characters

ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

JobLevel
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1
656 
2
617 
3
235 
4
94 
5
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row5

Common Values

ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

Length

2023-03-06T21:44:33.472756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:33.797707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

Most occurring characters

ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 656
39.1%
2 617
36.8%
3 235
 
14.0%
4 94
 
5.6%
5 75
 
4.5%

JobRole
Categorical

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Sales Executive
355 
Research Scientist
344 
Laboratory Technician
334 
Manufacturing Director
196 
Healthcare Representative
155 
Other values (4)
293 

Length

Max length25
Median length21
Mean length18.337507
Min length7

Characters and Unicode

Total characters30752
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowSales Representative
3rd rowSales Executive
4th rowHealthcare Representative
5th rowManager

Common Values

ValueCountFrequency (%)
Sales Executive 355
21.2%
Research Scientist 344
20.5%
Laboratory Technician 334
19.9%
Manufacturing Director 196
11.7%
Healthcare Representative 155
9.2%
Manager 111
 
6.6%
Sales Representative 77
 
4.6%
Research Director 71
 
4.2%
Human Resources 34
 
2.0%

Length

2023-03-06T21:44:34.098874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:34.487529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sales 432
13.3%
research 415
12.8%
executive 355
10.9%
scientist 344
10.6%
laboratory 334
10.3%
technician 334
10.3%
director 267
8.2%
representative 232
7.2%
manufacturing 196
6.0%
healthcare 155
 
4.8%
Other values (3) 179
5.5%

Most occurring characters

ValueCountFrequency (%)
e 4334
14.1%
a 3039
 
9.9%
t 2459
 
8.0%
c 2434
 
7.9%
i 2406
 
7.8%
r 2345
 
7.6%
n 1781
 
5.8%
1566
 
5.1%
s 1491
 
4.8%
o 969
 
3.2%
Other values (19) 7928
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25943
84.4%
Uppercase Letter 3243
 
10.5%
Space Separator 1566
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4334
16.7%
a 3039
11.7%
t 2459
9.5%
c 2434
9.4%
i 2406
9.3%
r 2345
9.0%
n 1781
6.9%
s 1491
 
5.7%
o 969
 
3.7%
h 904
 
3.5%
Other values (10) 3781
14.6%
Uppercase Letter
ValueCountFrequency (%)
S 776
23.9%
R 681
21.0%
E 355
10.9%
L 334
10.3%
T 334
10.3%
M 307
 
9.5%
D 267
 
8.2%
H 189
 
5.8%
Space Separator
ValueCountFrequency (%)
1566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29186
94.9%
Common 1566
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4334
14.8%
a 3039
10.4%
t 2459
 
8.4%
c 2434
 
8.3%
i 2406
 
8.2%
r 2345
 
8.0%
n 1781
 
6.1%
s 1491
 
5.1%
o 969
 
3.3%
h 904
 
3.1%
Other values (18) 7024
24.1%
Common
ValueCountFrequency (%)
1566
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4334
14.1%
a 3039
 
9.9%
t 2459
 
8.0%
c 2434
 
7.9%
i 2406
 
7.8%
r 2345
 
7.6%
n 1781
 
5.8%
1566
 
5.1%
s 1491
 
4.8%
o 969
 
3.2%
Other values (19) 7928
25.8%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
4
561 
3
516 
1
310 
2
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Length

2023-03-06T21:44:34.880377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:35.785403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring characters

ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Married
773 
Single
577 
Divorced
327 

Length

Max length8
Median length7
Mean length6.8509243
Min length6

Characters and Unicode

Total characters11489
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowDivorced
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 773
46.1%
Single 577
34.4%
Divorced 327
19.5%

Length

2023-03-06T21:44:36.078903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:36.393249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
married 773
46.1%
single 577
34.4%
divorced 327
19.5%

Most occurring characters

ValueCountFrequency (%)
r 1873
16.3%
i 1677
14.6%
e 1677
14.6%
d 1100
9.6%
M 773
6.7%
a 773
6.7%
S 577
 
5.0%
n 577
 
5.0%
g 577
 
5.0%
l 577
 
5.0%
Other values (4) 1308
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9812
85.4%
Uppercase Letter 1677
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1873
19.1%
i 1677
17.1%
e 1677
17.1%
d 1100
11.2%
a 773
7.9%
n 577
 
5.9%
g 577
 
5.9%
l 577
 
5.9%
v 327
 
3.3%
o 327
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
M 773
46.1%
S 577
34.4%
D 327
19.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 11489
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1873
16.3%
i 1677
14.6%
e 1677
14.6%
d 1100
9.6%
M 773
6.7%
a 773
6.7%
S 577
 
5.0%
n 577
 
5.0%
g 577
 
5.0%
l 577
 
5.0%
Other values (4) 1308
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1873
16.3%
i 1677
14.6%
e 1677
14.6%
d 1100
9.6%
M 773
6.7%
a 773
6.7%
S 577
 
5.0%
n 577
 
5.0%
g 577
 
5.0%
l 577
 
5.0%
Other values (4) 1308
11.4%

MonthlyIncome
Real number (ℝ)

Distinct895
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6196.0495
Minimum1010
Maximum19973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:36.707653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1010
5-th percentile2096
Q12875
median4834
Q37403
95-th percentile17610.2
Maximum19973
Range18963
Interquartile range (IQR)4528

Descriptive statistics

Standard deviation4520.0508
Coefficient of variation (CV)0.72950527
Kurtosis1.6846948
Mean6196.0495
Median Absolute Deviation (MAD)2013
Skewness1.5514099
Sum10390775
Variance20430859
MonotonicityNot monotonic
2023-03-06T21:44:37.061527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2911 9
 
0.5%
2559 9
 
0.5%
6811 8
 
0.5%
5228 8
 
0.5%
2342 8
 
0.5%
3646 8
 
0.5%
5473 7
 
0.4%
7441 7
 
0.4%
5207 6
 
0.4%
6272 6
 
0.4%
Other values (885) 1601
95.5%
ValueCountFrequency (%)
1010 1
 
0.1%
1081 4
0.2%
1091 3
0.2%
1200 1
 
0.1%
1223 1
 
0.1%
1232 1
 
0.1%
1261 3
0.2%
1274 4
0.2%
1281 4
0.2%
1359 2
0.1%
ValueCountFrequency (%)
19973 1
 
0.1%
19943 2
0.1%
19859 3
0.2%
19847 3
0.2%
19665 1
 
0.1%
19658 1
 
0.1%
19636 1
 
0.1%
19627 1
 
0.1%
19626 2
0.1%
19613 1
 
0.1%

MonthlyRate
Real number (ℝ)

Distinct903
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14770.048
Minimum636
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:37.313558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum636
5-th percentile3481.2
Q18509
median15332
Q320990
95-th percentile25761
Maximum26999
Range26363
Interquartile range (IQR)12481

Descriptive statistics

Standard deviation7112.2039
Coefficient of variation (CV)0.48152882
Kurtosis-1.207622
Mean14770.048
Median Absolute Deviation (MAD)6236
Skewness-0.061418209
Sum24769371
Variance50583444
MonotonicityNot monotonic
2023-03-06T21:44:37.613601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 9
 
0.5%
8952 8
 
0.5%
15891 8
 
0.5%
22490 7
 
0.4%
16900 7
 
0.4%
9150 7
 
0.4%
11652 7
 
0.4%
20364 6
 
0.4%
26999 6
 
0.4%
20439 6
 
0.4%
Other values (893) 1606
95.8%
ValueCountFrequency (%)
636 1
 
0.1%
2125 2
0.1%
2137 1
 
0.1%
2253 2
0.1%
2323 2
0.1%
2326 2
0.1%
2338 3
0.2%
2354 3
0.2%
2373 3
0.2%
2396 3
0.2%
ValueCountFrequency (%)
26999 6
0.4%
26959 2
 
0.1%
26897 3
0.2%
26894 1
 
0.1%
26862 2
 
0.1%
26849 2
 
0.1%
26767 1
 
0.1%
26703 1
 
0.1%
26589 4
0.2%
26551 3
0.2%

NumCompaniesWorked
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7257007
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:37.880366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3456941
Coefficient of variation (CV)0.86058388
Kurtosis0.49636451
Mean2.7257007
Median Absolute Deviation (MAD)0
Skewness1.2754566
Sum4571
Variance5.5022806
MonotonicityNot monotonic
2023-03-06T21:44:38.060836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 859
51.2%
2 169
 
10.1%
3 166
 
9.9%
4 161
 
9.6%
7 88
 
5.2%
6 68
 
4.1%
9 63
 
3.8%
5 61
 
3.6%
8 42
 
2.5%
ValueCountFrequency (%)
1 859
51.2%
2 169
 
10.1%
3 166
 
9.9%
4 161
 
9.6%
5 61
 
3.6%
6 68
 
4.1%
7 88
 
5.2%
8 42
 
2.5%
9 63
 
3.8%
ValueCountFrequency (%)
9 63
 
3.8%
8 42
 
2.5%
7 88
 
5.2%
6 68
 
4.1%
5 61
 
3.6%
4 161
 
9.6%
3 166
 
9.9%
2 169
 
10.1%
1 859
51.2%

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
False
1277 
True
400 
ValueCountFrequency (%)
False 1277
76.1%
True 400
 
23.9%
2023-03-06T21:44:38.345055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.903399
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:38.564862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q317
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4208011
Coefficient of variation (CV)0.2295316
Kurtosis-0.063513479
Mean14.903399
Median Absolute Deviation (MAD)2
Skewness0.91450964
Sum24993
Variance11.70188
MonotonicityNot monotonic
2023-03-06T21:44:38.816440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13 267
15.9%
14 258
15.4%
11 237
14.1%
12 235
14.0%
15 124
7.4%
18 97
 
5.8%
17 92
 
5.5%
19 79
 
4.7%
16 74
 
4.4%
22 57
 
3.4%
Other values (5) 157
9.4%
ValueCountFrequency (%)
11 237
14.1%
12 235
14.0%
13 267
15.9%
14 258
15.4%
15 124
7.4%
16 74
 
4.4%
17 92
 
5.5%
18 97
 
5.8%
19 79
 
4.7%
20 55
 
3.3%
ValueCountFrequency (%)
25 10
 
0.6%
24 16
 
1.0%
23 22
 
1.3%
22 57
3.4%
21 54
3.2%
20 55
3.3%
19 79
4.7%
18 97
5.8%
17 92
5.5%
16 74
4.4%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
523 
4
518 
2
337 
1
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Length

2023-03-06T21:44:39.108013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:39.408324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring characters

ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1
745 
0
732 
2
135 
3
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Length

2023-03-06T21:44:39.705524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:39.994507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

TotalWorkingYears
Real number (ℝ)

Distinct41
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.7096
Minimum0
Maximum41
Zeros13
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:40.311027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q314
95-th percentile26
Maximum41
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.2551346
Coefficient of variation (CV)0.67744213
Kurtosis1.1838082
Mean10.7096
Median Absolute Deviation (MAD)4
Skewness1.1452338
Sum17960
Variance52.636978
MonotonicityNot monotonic
2023-03-06T21:44:40.616583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10 254
15.1%
6 156
 
9.3%
8 116
 
6.9%
9 114
 
6.8%
7 104
 
6.2%
1 103
 
6.1%
5 94
 
5.6%
4 77
 
4.6%
12 54
 
3.2%
15 53
 
3.2%
Other values (31) 552
32.9%
ValueCountFrequency (%)
0 13
 
0.8%
1 103
6.1%
2 30
 
1.8%
3 48
 
2.9%
4 77
4.6%
5 94
5.6%
6 156
9.3%
7 104
6.2%
8 116
6.9%
9 114
6.8%
ValueCountFrequency (%)
41 1
 
0.1%
40 1
 
0.1%
38 2
 
0.1%
37 2
 
0.1%
36 2
 
0.1%
35 1
 
0.1%
34 4
0.2%
33 8
0.5%
32 2
 
0.1%
31 9
0.5%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7328563
Minimum0
Maximum6
Zeros50
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:40.898380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1452714
Coefficient of variation (CV)0.41907487
Kurtosis1.0078225
Mean2.7328563
Median Absolute Deviation (MAD)1
Skewness0.57095907
Sum4583
Variance1.3116466
MonotonicityNot monotonic
2023-03-06T21:44:41.121912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 655
39.1%
3 626
37.3%
5 122
 
7.3%
4 122
 
7.3%
1 63
 
3.8%
0 50
 
3.0%
6 39
 
2.3%
ValueCountFrequency (%)
0 50
 
3.0%
1 63
 
3.8%
2 655
39.1%
3 626
37.3%
4 122
 
7.3%
5 122
 
7.3%
6 39
 
2.3%
ValueCountFrequency (%)
6 39
 
2.3%
5 122
 
7.3%
4 122
 
7.3%
3 626
37.3%
2 655
39.1%
1 63
 
3.8%
0 50
 
3.0%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
1089 
2
385 
4
135 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Length

2023-03-06T21:44:41.374448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:41.672621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring characters

ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8067979
Minimum0
Maximum41
Zeros54
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:41.957097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum41
Range41
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8832821
Coefficient of variation (CV)0.8643245
Kurtosis3.9297715
Mean6.8067979
Median Absolute Deviation (MAD)3
Skewness1.7382885
Sum11415
Variance34.613009
MonotonicityNot monotonic
2023-03-06T21:44:42.240892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 228
13.6%
1 214
12.8%
3 176
10.5%
10 138
8.2%
4 121
 
7.2%
2 108
 
6.4%
7 103
 
6.1%
9 99
 
5.9%
8 91
 
5.4%
6 87
 
5.2%
Other values (24) 312
18.6%
ValueCountFrequency (%)
0 54
 
3.2%
1 214
12.8%
2 108
6.4%
3 176
10.5%
4 121
7.2%
5 228
13.6%
6 87
 
5.2%
7 103
6.1%
8 91
 
5.4%
9 99
5.9%
ValueCountFrequency (%)
41 1
 
0.1%
37 1
 
0.1%
34 2
 
0.1%
33 5
0.3%
31 4
0.2%
30 2
 
0.1%
29 3
0.2%
27 2
 
0.1%
26 1
 
0.1%
25 5
0.3%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1443053
Minimum0
Maximum18
Zeros307
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:42.525339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5833977
Coefficient of variation (CV)0.86465581
Kurtosis0.68077127
Mean4.1443053
Median Absolute Deviation (MAD)3
Skewness0.94266312
Sum6950
Variance12.840739
MonotonicityNot monotonic
2023-03-06T21:44:42.775684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 415
24.7%
0 307
18.3%
7 263
15.7%
3 166
 
9.9%
4 125
 
7.5%
8 103
 
6.1%
9 77
 
4.6%
1 43
 
2.6%
6 40
 
2.4%
5 34
 
2.0%
Other values (9) 104
 
6.2%
ValueCountFrequency (%)
0 307
18.3%
1 43
 
2.6%
2 415
24.7%
3 166
 
9.9%
4 125
 
7.5%
5 34
 
2.0%
6 40
 
2.4%
7 263
15.7%
8 103
 
6.1%
9 77
 
4.6%
ValueCountFrequency (%)
18 4
 
0.2%
17 6
 
0.4%
16 6
 
0.4%
15 5
 
0.3%
14 14
 
0.8%
13 14
 
0.8%
12 11
 
0.7%
11 16
 
1.0%
10 28
 
1.7%
9 77
4.6%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9785331
Minimum0
Maximum15
Zeros726
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:43.053036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8.2
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0457164
Coefficient of variation (CV)1.5393811
Kurtosis4.1578175
Mean1.9785331
Median Absolute Deviation (MAD)1
Skewness2.0807537
Sum3318
Variance9.2763885
MonotonicityNot monotonic
2023-03-06T21:44:43.290922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 726
43.3%
1 407
24.3%
2 148
 
8.8%
7 89
 
5.3%
4 78
 
4.7%
3 53
 
3.2%
5 41
 
2.4%
6 36
 
2.1%
11 31
 
1.8%
8 15
 
0.9%
Other values (6) 53
 
3.2%
ValueCountFrequency (%)
0 726
43.3%
1 407
24.3%
2 148
 
8.8%
3 53
 
3.2%
4 78
 
4.7%
5 41
 
2.4%
6 36
 
2.1%
7 89
 
5.3%
8 15
 
0.9%
9 15
 
0.9%
ValueCountFrequency (%)
15 12
 
0.7%
14 6
 
0.4%
13 8
 
0.5%
12 8
 
0.5%
11 31
 
1.8%
10 4
 
0.2%
9 15
 
0.9%
8 15
 
0.9%
7 89
5.3%
6 36
2.1%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1848539
Minimum0
Maximum17
Zeros298
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:43.585949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5750307
Coefficient of variation (CV)0.85427849
Kurtosis0.17066667
Mean4.1848539
Median Absolute Deviation (MAD)3
Skewness0.8193449
Sum7018
Variance12.780844
MonotonicityNot monotonic
2023-03-06T21:44:43.836955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 387
23.1%
0 298
17.8%
7 265
15.8%
3 174
10.4%
8 125
 
7.5%
4 107
 
6.4%
1 71
 
4.2%
9 57
 
3.4%
5 38
 
2.3%
6 34
 
2.0%
Other values (8) 121
 
7.2%
ValueCountFrequency (%)
0 298
17.8%
1 71
 
4.2%
2 387
23.1%
3 174
10.4%
4 107
 
6.4%
5 38
 
2.3%
6 34
 
2.0%
7 265
15.8%
8 125
 
7.5%
9 57
 
3.4%
ValueCountFrequency (%)
17 8
 
0.5%
16 2
 
0.1%
15 6
 
0.4%
14 7
 
0.4%
13 17
 
1.0%
12 22
 
1.3%
11 30
 
1.8%
10 29
 
1.7%
9 57
3.4%
8 125
7.5%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
0
1477 
1
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

Length

2023-03-06T21:44:44.115149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:44.413075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1477
88.1%
1 200
 
11.9%

DistanceFromHome_out
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
0
1624 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Length

2023-03-06T21:44:44.663598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:44.947994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1624
96.8%
1 53
 
3.2%

Age_out
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
0
1655 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

Length

2023-03-06T21:44:45.176444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:45.461586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1655
98.7%
1 22
 
1.3%

CurrManagerTotal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct174
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41016766
Minimum0
Maximum1
Zeros298
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:45.744558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15789474
median0.4
Q30.66666667
95-th percentile0.88888889
Maximum1
Range1
Interquartile range (IQR)0.50877193

Descriptive statistics

Standard deviation0.29709736
Coefficient of variation (CV)0.72433149
Kurtosis-1.1263733
Mean0.41016766
Median Absolute Deviation (MAD)0.26666667
Skewness0.1493561
Sum687.85117
Variance0.088266839
MonotonicityNot monotonic
2023-03-06T21:44:46.118941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 298
17.8%
0.5 120
 
7.2%
0.3333333333 88
 
5.2%
0.6666666667 83
 
4.9%
0.8 80
 
4.8%
0.7 73
 
4.4%
0.25 59
 
3.5%
1 48
 
2.9%
0.4 45
 
2.7%
0.2 45
 
2.7%
Other values (164) 738
44.0%
ValueCountFrequency (%)
0 298
17.8%
0.02941176471 1
 
0.1%
0.03571428571 1
 
0.1%
0.03703703704 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.1%
0.05263157895 3
 
0.2%
0.05882352941 3
 
0.2%
0.0625 3
 
0.2%
0.06666666667 6
 
0.4%
ValueCountFrequency (%)
1 48
2.9%
0.9375 1
 
0.1%
0.9230769231 2
 
0.1%
0.9166666667 1
 
0.1%
0.9090909091 1
 
0.1%
0.9 20
1.2%
0.8888888889 12
 
0.7%
0.8823529412 1
 
0.1%
0.875 31
1.8%
0.8571428571 8
 
0.5%

TotalSatisfaction
Real number (ℝ)

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7708706
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:46.431650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median2.75
Q33
95-th percentile3.5
Maximum4
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.49370714
Coefficient of variation (CV)0.17817762
Kurtosis-0.029500778
Mean2.7708706
Median Absolute Deviation (MAD)0.25
Skewness-0.2455635
Sum4646.75
Variance0.24374674
MonotonicityNot monotonic
2023-03-06T21:44:46.683893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2.75 356
21.2%
3 303
18.1%
2.5 272
16.2%
3.25 219
13.1%
2.25 175
10.4%
3.5 131
 
7.8%
2 101
 
6.0%
3.75 49
 
2.9%
1.75 38
 
2.3%
1.5 21
 
1.3%
Other values (3) 12
 
0.7%
ValueCountFrequency (%)
1 2
 
0.1%
1.25 4
 
0.2%
1.5 21
 
1.3%
1.75 38
 
2.3%
2 101
 
6.0%
2.25 175
10.4%
2.5 272
16.2%
2.75 356
21.2%
3 303
18.1%
3.25 219
13.1%
ValueCountFrequency (%)
4 6
 
0.4%
3.75 49
 
2.9%
3.5 131
 
7.8%
3.25 219
13.1%
3 303
18.1%
2.75 356
21.2%
2.5 272
16.2%
2.25 175
10.4%
2 101
 
6.0%
1.75 38
 
2.3%

YearsInJobs
Real number (ℝ)

Distinct154
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0578241
Minimum0
Maximum41
Zeros13
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:44:47.004390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.1428571
median5
Q39
95-th percentile16
Maximum41
Range41
Interquartile range (IQR)6.8571429

Descriptive statistics

Standard deviation5.3982349
Coefficient of variation (CV)0.8911178
Kurtosis5.9949779
Mean6.0578241
Median Absolute Deviation (MAD)3
Skewness2.0160952
Sum10158.971
Variance29.14094
MonotonicityNot monotonic
2023-03-06T21:44:47.334502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 162
 
9.7%
1 144
 
8.6%
6 119
 
7.1%
5 111
 
6.6%
4 90
 
5.4%
2 71
 
4.2%
3 70
 
4.2%
9 68
 
4.1%
8 49
 
2.9%
7 43
 
2.6%
Other values (144) 750
44.7%
ValueCountFrequency (%)
0 13
0.8%
0.2222222222 1
 
0.1%
0.3333333333 1
 
0.1%
0.375 1
 
0.1%
0.4444444444 2
 
0.1%
0.5 11
0.7%
0.5714285714 2
 
0.1%
0.625 1
 
0.1%
0.6666666667 16
1.0%
0.7142857143 4
 
0.2%
ValueCountFrequency (%)
41 1
 
0.1%
38 1
 
0.1%
34 4
0.2%
33 3
0.2%
31 3
0.2%
29 1
 
0.1%
28 2
 
0.1%
26 3
0.2%
25 1
 
0.1%
24 5
0.3%

JobHopper
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
0
1467 
1
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Length

2023-03-06T21:44:47.650341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:44:47.925362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1467
87.5%
1 210
 
12.5%

Interactions

2023-03-06T21:44:16.874046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:50.743551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:54.739929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:00.208014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:05.164226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:10.471877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:15.093835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:19.777703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:25.665313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:31.402745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:36.687277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:42.951571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:48.818730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:54.594538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:59.319195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:05.057108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:10.848902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:17.218880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:50.975368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:54.999872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:00.440515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:05.480394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:10.738366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:15.390992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:20.017843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:25.985765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:31.719479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:37.043003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:43.254122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:49.163697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:54.849632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:59.603357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:05.317768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:11.169205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:17.627793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:51.296133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:55.338995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:00.759862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:05.838663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:11.058011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:15.699034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:20.312439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:26.349664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:32.058690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:37.453503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:43.963845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:49.584334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:55.144997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:59.891867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:05.564692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:11.534287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:18.020666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:51.585751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:55.666248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:01.014792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:06.080590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:11.314519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:15.948159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:20.605482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:26.677685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:32.369964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:37.877192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:44.294675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:50.038942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:55.451678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:00.182280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:06.212280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:11.868655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:18.363506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:51.891706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:55.951359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:01.299600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:06.387268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:11.557394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:16.214989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:20.883378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:27.025258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:32.690809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:38.302313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:44.592994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:50.625726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:55.689805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:00.475622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:06.513129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:12.232138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:18.766749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:52.162660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:56.288981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:01.603981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:06.726426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:11.877309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:16.494977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:21.173871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:27.373695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:33.032785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:38.717539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:44.935864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:51.111461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:55.964301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:00.784982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:06.872492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:12.589173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:19.127514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:52.441764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:56.613388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:01.843851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:07.030876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:12.198323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:16.753004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:21.445251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:27.708570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:33.281402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:39.077732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:45.212810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:51.492975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:56.221709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:01.065537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:07.163748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:12.915715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:19.529630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:52.808213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:56.886754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:02.137363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:07.346402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:12.525304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:17.048908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:21.788084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:28.054284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:33.555023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:39.487653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:45.569516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:51.993789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:56.507363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:01.406390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:07.520930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:13.284419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:19.899725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.042063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:57.207537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:02.440279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:07.621861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:12.657541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:17.345632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:22.070695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:28.411962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:33.777845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:39.850549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:45.886595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:52.485999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:56.813531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:01.740954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:07.861624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:13.631067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:20.191827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.145790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:57.508392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:02.736935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:07.880877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:12.791386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:17.588935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:22.419615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:28.726604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:34.050561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:40.159048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:46.167362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:52.841702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:57.098855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:02.087763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:08.161638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:13.941861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:20.533715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.245213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:57.838545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:03.023295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:08.170339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:12.935560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:17.843058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:22.865989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:29.058298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:34.305606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:40.488879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:46.488809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:53.092074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:57.371018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:02.455256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:08.469865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:14.258585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:20.846700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.328296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:58.096225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:03.290787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:08.428641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:13.170087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:18.055930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:23.204902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:29.397850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:34.606673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:40.850104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:46.794145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:53.309857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:57.636195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:02.788342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:08.772552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:14.588907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:21.202626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.425419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:58.421230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:03.616364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:08.789729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:13.484172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:18.339724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:23.878800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:29.740039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:34.965602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:41.242000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:47.126983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:53.567868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:57.935732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:03.165420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:09.124456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:14.950434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:21.533911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.704687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:58.748356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:03.934131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:09.127059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:13.814345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:18.620387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:24.269689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:30.072683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:35.302929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:41.588084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:47.444253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:53.825020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:58.217977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:03.555288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:09.467810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:15.307069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:21.912895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:53.964511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:59.271088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:04.268386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:09.434892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:14.173854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:18.923261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:24.623482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:30.426755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:35.661802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:41.933408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:47.818517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:53.976938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:58.473088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:03.928712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:09.842369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:15.706543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:22.209284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:54.214724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:59.569575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:04.571928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:09.729500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:14.488295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:19.220439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:24.976334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:30.748848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:35.998849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:42.300663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:48.139216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:54.149573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:58.747535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:04.264735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:10.198162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:16.072495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:22.564267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:54.495778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:42:59.878598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:04.898817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:10.013618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:14.841546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:19.461212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:25.336236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:31.092561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:36.344935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:42.671535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:48.452967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:54.365669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:43:59.049303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:04.732392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:10.534796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:44:16.493259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-06T21:44:48.265232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerCurrManagerTotalTotalSatisfactionYearsInJobsBusinessTravelDepartmentEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusOverTimeRelationshipSatisfactionStockOptionLevelWorkLifeBalanceAttritionDistanceFromHome_outAge_outJobHopper
Age1.0000.035-0.0340.0320.4200.0070.407-0.0530.6360.0320.2500.2150.1780.197-0.1970.0440.2040.0430.0350.1730.0420.0470.0500.0000.2850.1830.0000.1200.0110.0220.0960.0000.1640.0000.7750.105
DailyRate0.0351.0000.0180.0010.023-0.012-0.020-0.0110.043-0.0300.0680.0590.0390.0410.019-0.0310.0680.0570.0000.0000.0000.0000.0290.0620.0240.0000.0000.0000.0010.0130.0000.0000.0000.0000.0000.000
DistanceFromHome-0.0340.0181.000-0.003-0.0520.027-0.0210.017-0.0180.006-0.014-0.010-0.0170.0070.0210.0030.0020.0000.0000.0000.0000.0160.0080.0000.0720.0320.0000.0300.0000.0000.0440.0000.0000.9190.0000.092
HourlyRate0.0320.001-0.0031.000-0.032-0.0190.0690.021-0.0310.058-0.081-0.079-0.043-0.075-0.078-0.028-0.0870.0400.0590.0000.0380.0310.0000.0130.0000.0300.0290.0950.0600.0460.0620.0400.0390.0000.0000.000
MonthlyIncome0.4200.023-0.052-0.0321.0000.0440.160-0.1030.654-0.0160.4880.4250.2920.4140.0610.0090.4290.0000.2030.0970.1000.0000.0000.0000.8480.4110.0570.0140.0570.0220.0280.0460.1940.0620.1090.118
MonthlyRate0.007-0.0120.027-0.0190.0441.000-0.0170.0490.0050.0260.0290.0350.0130.004-0.011-0.0100.0190.0000.0370.0410.0000.0490.0700.0000.0490.0400.0000.0000.0000.0300.0000.0230.0550.0410.0000.000
NumCompaniesWorked0.407-0.020-0.0210.0690.160-0.0171.000-0.0300.3310.013-0.188-0.141-0.047-0.145-0.381-0.037-0.5350.0000.0170.0820.0110.0290.0520.0000.1180.0670.0230.0560.0000.0190.0000.0300.1130.0560.1100.797
PercentSalaryHike-0.053-0.0110.0170.021-0.1030.049-0.0301.000-0.063-0.010-0.072-0.046-0.091-0.054-0.013-0.022-0.0270.0580.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0440.0200.0520.0400.0800.0650.0000.000
TotalWorkingYears0.6360.043-0.018-0.0310.6540.0050.331-0.0631.000-0.0300.5960.5160.3560.531-0.0020.0110.5520.0370.1010.1030.0690.0510.1260.0600.5290.3010.0000.0630.0200.0000.0500.0000.1790.0440.2360.204
TrainingTimesLastYear0.032-0.0300.0060.058-0.0160.0260.013-0.010-0.0301.000-0.063-0.069-0.023-0.060-0.054-0.029-0.0350.0000.0640.0000.0400.0140.0350.0000.0270.0500.0000.0300.0420.0000.0000.0000.0810.0000.0600.000
YearsAtCompany0.2500.068-0.014-0.0810.4880.029-0.188-0.0720.596-0.0631.0000.8670.5170.8740.5590.0170.7060.0410.0420.0850.0530.0160.0320.0450.3470.2020.0000.0000.0000.0320.0000.0000.1370.0000.0000.227
YearsInCurrentRole0.2150.059-0.010-0.0790.4250.035-0.141-0.0460.516-0.0690.8671.0000.4970.7810.5310.0050.6010.0960.0000.0720.0000.0000.0000.0000.2320.1410.0000.0000.0000.0170.0360.0000.1720.0000.0000.226
YearsSinceLastPromotion0.1780.039-0.017-0.0430.2920.013-0.047-0.0910.356-0.0230.5170.4971.0000.4870.3190.0760.3540.0000.0420.0470.0380.0000.0620.0210.2250.1320.0000.0000.0000.0480.0000.0000.0000.0590.0790.139
YearsWithCurrManager0.1970.0410.007-0.0750.4140.004-0.145-0.0540.531-0.0600.8740.7810.4871.0000.7530.0280.6150.0340.0500.0530.0360.0000.0000.0260.2270.1310.0670.0000.0560.0000.0000.0000.1740.0000.0000.214
CurrManagerTotal-0.1970.0190.021-0.0780.061-0.011-0.381-0.013-0.002-0.0540.5590.5310.3190.7531.0000.0440.4080.0470.0000.0570.0000.0300.0000.0100.1660.1150.0000.0790.0000.0150.0300.0150.1570.0840.0640.204
TotalSatisfaction0.044-0.0310.003-0.0280.009-0.010-0.037-0.0220.011-0.0290.0170.0050.0760.0280.0441.0000.0460.0270.0000.0290.0000.3090.0710.2530.0000.0110.3300.0000.0000.3330.0000.0590.1970.0000.0300.000
YearsInJobs0.2040.0680.002-0.0870.4290.019-0.535-0.0270.552-0.0350.7060.6010.3540.6150.4080.0461.0000.0000.0000.0360.0420.0000.0590.0650.2730.1710.0000.0000.0000.0390.0140.0000.1610.0000.0050.396
BusinessTravel0.0430.0570.0000.0400.0000.0000.0000.0580.0370.0000.0410.0960.0000.0340.0470.0270.0001.0000.0000.0000.0390.0000.0000.0410.0410.0290.0210.0140.0230.0000.0270.0000.1110.0000.0060.000
Department0.0350.0000.0000.0590.2030.0370.0170.0000.1010.0640.0420.0000.0420.0500.0000.0000.0000.0001.0000.0000.5440.0000.0000.0000.2050.9340.0480.0240.0000.0000.0000.0000.0430.0000.0000.000
Education0.1730.0000.0000.0000.0970.0410.0820.0000.1030.0000.0850.0720.0470.0530.0570.0290.0360.0000.0001.0000.0430.0490.0000.0210.0920.0640.0290.0440.0000.0000.0520.0000.0920.0000.0130.042
EducationField0.0420.0000.0000.0380.1000.0000.0110.0000.0690.0400.0530.0000.0380.0360.0000.0000.0420.0390.5440.0431.0000.0000.0000.0260.0910.3040.0170.0390.0400.0000.0000.0340.0390.0000.0380.000
EnvironmentSatisfaction0.0470.0000.0160.0310.0000.0490.0290.0000.0510.0140.0160.0000.0000.0000.0300.3090.0000.0000.0000.0490.0001.0000.0380.0210.0000.0360.0230.0100.0000.0260.0190.0000.0920.0280.0320.030
Gender0.0500.0290.0080.0000.0000.0700.0520.0000.1260.0350.0320.0000.0620.0000.0000.0710.0590.0000.0000.0000.0000.0381.0000.0000.0730.1140.0160.0000.0000.0010.0000.0520.0320.0000.0420.000
JobInvolvement0.0000.0620.0000.0130.0000.0000.0000.0000.0600.0000.0450.0000.0210.0260.0100.2530.0650.0410.0000.0210.0260.0210.0001.0000.0000.0260.0210.0000.0470.0000.0260.0250.1560.0000.0000.022
JobLevel0.2850.0240.0720.0000.8480.0490.1180.0530.5290.0270.3470.2320.2250.2270.1660.0000.2730.0410.2050.0920.0910.0000.0730.0001.0000.5810.0000.0000.0140.0000.0340.0000.1790.0290.1320.127
JobRole0.1830.0000.0320.0300.4110.0400.0670.0000.3010.0500.2020.1410.1320.1310.1150.0110.1710.0290.9340.0640.3040.0360.1140.0260.5811.0000.0000.0000.0000.0260.0410.0000.2050.0370.0790.112
JobSatisfaction0.0000.0000.0000.0290.0570.0000.0230.0000.0000.0000.0000.0000.0000.0670.0000.3300.0000.0210.0480.0290.0170.0230.0160.0210.0000.0001.0000.0000.0000.0150.0000.0000.0600.0000.0000.000
MaritalStatus0.1200.0000.0300.0950.0140.0000.0560.0000.0630.0300.0000.0000.0000.0000.0790.0000.0000.0140.0240.0440.0390.0100.0000.0000.0000.0000.0001.0000.0440.0000.6070.0000.1780.0170.0000.044
OverTime0.0110.0010.0000.0600.0570.0000.0000.0440.0200.0420.0000.0000.0000.0560.0000.0000.0000.0230.0000.0000.0400.0000.0000.0470.0140.0000.0000.0441.0000.0450.0430.0330.1700.0000.0000.000
RelationshipSatisfaction0.0220.0130.0000.0460.0220.0300.0190.0200.0000.0000.0320.0170.0480.0000.0150.3330.0390.0000.0000.0000.0000.0260.0010.0000.0000.0260.0150.0000.0451.0000.0150.0000.0910.0100.0000.000
StockOptionLevel0.0960.0000.0440.0620.0280.0000.0000.0520.0500.0000.0000.0360.0000.0000.0300.0000.0140.0270.0000.0520.0000.0190.0000.0260.0340.0410.0000.6070.0430.0151.0000.0310.2410.0310.0000.000
WorkLifeBalance0.0000.0000.0000.0400.0460.0230.0300.0400.0000.0000.0000.0000.0000.0000.0150.0590.0000.0000.0000.0000.0340.0000.0520.0250.0000.0000.0000.0000.0330.0000.0311.0000.0820.0000.0240.000
Attrition0.1640.0000.0000.0390.1940.0550.1130.0800.1790.0810.1370.1720.0000.1740.1570.1970.1610.1110.0430.0920.0390.0920.0320.1560.1790.2050.0600.1780.1700.0910.2410.0821.0000.0000.0240.047
DistanceFromHome_out0.0000.0000.9190.0000.0620.0410.0560.0650.0440.0000.0000.0000.0590.0000.0840.0000.0000.0000.0000.0000.0000.0280.0000.0000.0290.0370.0000.0170.0000.0100.0310.0000.0001.0000.0000.031
Age_out0.7750.0000.0000.0000.1090.0000.1100.0000.2360.0600.0000.0000.0790.0000.0640.0300.0050.0060.0000.0130.0380.0320.0420.0000.1320.0790.0000.0000.0000.0000.0000.0240.0240.0001.0000.026
JobHopper0.1050.0000.0920.0000.1180.0000.7970.0000.2040.0000.2270.2260.1390.2140.2040.0000.3960.0000.0000.0420.0000.0300.0000.0220.1270.1120.0000.0440.0000.0000.0000.0000.0470.0310.0261.000

Missing values

2023-03-06T21:44:23.226065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-06T21:44:25.783587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionDistanceFromHome_outAge_outCurrManagerTotalTotalSatisfactionYearsInJobsJobHopper
036Travel_Frequently599Research & Development243Medical4Male4231Laboratory Technician4Married259650991Yes13211023100780000.8000003.2510.000
135Travel_Rarely921Sales83Other1Male4631Sales Representative1Married2899107781No174143342030000.7500002.254.000
232Travel_Rarely718Sales263Marketing3Male8032Sales Executive4Divorced4627164951No174243332120000.5000003.504.000
338Travel_Rarely1488Research & Development23Medical3Female4032Healthcare Representative1Married5347133843No1430151160020000.1333332.505.000
450Travel_Rarely1017Research & Development54Medical2Female3735Manager1Single19033198051Yes1330310331144101000.3225812.2531.000
527Travel_Rarely566Research & Development23Other3Female5632Manufacturing Director2Single419771035No114060310100000.0000003.001.201
634Travel_Rarely944Research & Development104Medical2Male3631Laboratory Technician1Single1281169001No131012310000000.0000001.751.000
740Travel_Rarely1009Research & Development23Life Sciences4Male7431Laboratory Technician4Divorced3067129162No124163232120000.3333333.753.000
851Travel_Frequently1297Sales63Life Sciences4Male4312Sales Executive4Married6439212218Yes131118331614490000.5000002.502.250
925Travel_Rarely806Research & Development91Medical3Female8231Laboratory Technician3Married274179501No153192297780000.8888893.009.000
AgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionDistanceFromHome_outAge_outCurrManagerTotalTotalSatisfactionYearsInJobsJobHopper
166728Travel_Rarely207Research & Development12Medical4Female6611Laboratory Technician3Single2207262041No122035332120000.6666672.503.0000000
166844Travel_Rarely636Research & Development91Medical2Female9135Research Director3Divorced19627224566No1321230222220000.0869572.503.8333330
166944Travel_Rarely848Research & Development23Medical1Female6021Research Scientist4Single2372268499No194062322220000.3333332.750.6666671
167048Travel_Rarely776Research & Development233Life Sciences1Male9433Research Director4Single6673121474No1330234310000000.0000002.755.7500000
167155Travel_Rarely1276Research & Development164Medical1Male7822Healthcare Representative4Married599371293No17231233109670000.5833332.254.0000000
167230Travel_Rarely945Sales13Life Sciences4Female7333Sales Executive3Single8722142551No19201024100080000.8000003.0010.0000000
167332Travel_Rarely1303Research & Development23Life Sciences1Male4831Research Scientist2Married3544159724No1941103442130000.3000002.502.5000000
167429Travel_Frequently1184Human Resources243Human Resources2Male3621Human Resources1Married2804153221Yes113012310001000.0000002.001.0000000
167536Travel_Rarely441Sales92Marketing2Male4842Sales Executive3Divorced540640511No21321032103080000.8000003.0010.0000000
167636Travel_Rarely1141Research & Development203Life Sciences3Female3531Laboratory Technician3Single2593173811No1940103282730000.3000003.2510.0000000